Cargando…

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to no...

Descripción completa

Detalles Bibliográficos
Autores principales: Calza, Stefano, Raffelsberger, Wolfgang, Ploner, Alexander, Sahel, Jose, Leveillard, Thierry, Pawitan, Yudi
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2018638/
https://www.ncbi.nlm.nih.gov/pubmed/17702762
http://dx.doi.org/10.1093/nar/gkm537
_version_ 1782136577421475840
author Calza, Stefano
Raffelsberger, Wolfgang
Ploner, Alexander
Sahel, Jose
Leveillard, Thierry
Pawitan, Yudi
author_facet Calza, Stefano
Raffelsberger, Wolfgang
Ploner, Alexander
Sahel, Jose
Leveillard, Thierry
Pawitan, Yudi
author_sort Calza, Stefano
collection PubMed
description Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article.
format Text
id pubmed-2018638
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-20186382007-10-23 Filtering genes to improve sensitivity in oligonucleotide microarray data analysis Calza, Stefano Raffelsberger, Wolfgang Ploner, Alexander Sahel, Jose Leveillard, Thierry Pawitan, Yudi Nucleic Acids Res Methods Online Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article. Oxford University Press 2007-08 2007-08-15 /pmc/articles/PMC2018638/ /pubmed/17702762 http://dx.doi.org/10.1093/nar/gkm537 Text en © 2007 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Calza, Stefano
Raffelsberger, Wolfgang
Ploner, Alexander
Sahel, Jose
Leveillard, Thierry
Pawitan, Yudi
Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
title Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
title_full Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
title_fullStr Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
title_full_unstemmed Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
title_short Filtering genes to improve sensitivity in oligonucleotide microarray data analysis
title_sort filtering genes to improve sensitivity in oligonucleotide microarray data analysis
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2018638/
https://www.ncbi.nlm.nih.gov/pubmed/17702762
http://dx.doi.org/10.1093/nar/gkm537
work_keys_str_mv AT calzastefano filteringgenestoimprovesensitivityinoligonucleotidemicroarraydataanalysis
AT raffelsbergerwolfgang filteringgenestoimprovesensitivityinoligonucleotidemicroarraydataanalysis
AT ploneralexander filteringgenestoimprovesensitivityinoligonucleotidemicroarraydataanalysis
AT saheljose filteringgenestoimprovesensitivityinoligonucleotidemicroarraydataanalysis
AT leveillardthierry filteringgenestoimprovesensitivityinoligonucleotidemicroarraydataanalysis
AT pawitanyudi filteringgenestoimprovesensitivityinoligonucleotidemicroarraydataanalysis